Previously, we considered hierarchical models for solving problems with, so to speak, the classical approach of the Markov process. However, the advantages of using hierarchical approaches also apply to sequence analysis problems. One such algorithm is the Control Transformer presented in the article "Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling". The method authors position it as a new architecture designed to solve ...
In 1942, Ralph Hartley proposed an analogue of the Fourier transform in his article "A More Symmetrical Fourier Analysis Applied to Transmission Problems". Just like Fourier transform (FT), Hartley transform (HT) turns the original signal into a sum of trigonometric functions. But there is one significant difference between them. FT converts real values to complex numbers, while HT provides only real results. Because of this difference, the Hartley transform did not become popular - scientists ...
In the previous articles, we started developing a multi-currency EA that works simultaneously with various trading strategies. The solution provided in the second article is already significantly different from the one presented in the first one. This indicates that we are still in search of the best options. Let's try to look at the developed system as a whole, abstracting from the small details of the implementation, in order to understand ways to improve it. To do this, let ...
I sometimes receive private messages from those who want to learn how to create their own Expert Advisors or indicators. Although there is a lot of material on this site and on the Internet in general, including very good resources with examples, beginners still need help. Some users seek more consistency in presentation, others require clarity or something else. Sometimes users ask: "Add comments to the code of a working Expert Advisor, I will understand everything and make the same one myself!" ...
Recently, offline reinforcement learning methods have become widespread, which promises many prospects in solving problems of varying complexity. However, one of the main problems that researchers face is the optimism that can arise while learning. The agent optimizes its strategy based on the data from the training set and gains confidence in its actions. But the training set is quite often not able to cover the entire variety of possible states and transitions of the environment. In a stochastic ...
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